Long Range Planning 46 (2013) 419–426

Contents lists available at ScienceDirect

Long Range Planning

journal homepage: ht tp: / /www.elsevier .com/locate/ l rp

Business Models and Technological Innovation

Charles Baden-Fuller, Stefan Haefliger

Business models are fundamentally linked with technological innovation, yet the business model construct is essentially separable fromtechnology. We define the business model as a system that solves the problem of identifying who is (or are) the customer(s), engagingwith their needs, delivering satisfaction, and monetizing the value. The framework depicts the business model system as a modelcontaining cause and effect relationships, and it provides a basis for classification. We formulate the business model relationship withtechnology in a two-way manner. First, business models mediate the link between technology and firm performance. Secondly, devel-oping the right technology is a matter of a business model decision regarding openness and user engagement. We suggest researchquestions both for technology management and innovation, as well as strategy.

� 2013 Elsevier Ltd. All rights reserved.

Introduction

The business model construct has become attractive to many academics, taking on its own momentum as is evidenced bythe fact that, in the three years since publication, the Long Range Planning (2010) special issue on business models attractedmore than 150,000 downloads and more than 3,500 Google Scholar and more than 500 ISI citations. Yet, the construct hasalso attracted criticism. Zott et al., (2011) complain that business models “have yet to develop a common and widely acceptedlanguage that would allow researchers who examine the business model construct through different lenses to draw effec-tively on the work of others”, because there appears to be a diverse set of business model definitions and a diverse set ofapproaches to classification. These views reflect confusion that has taken energy away from proper dialogue on key questionsd What are the components of a business model, and how does business model innovation occur?

In this piece, we explore one clear emerging view of the business model construct, and examine where this view takes usin terms of understanding an issue referred to by authors such as Chesbrough (2003, 2010) concerning the relationshipbetween business model innovation and technical innovation. While researchers such as Osterwalder et al. (2010) and Demiland Lecocq (2010) have proposed that the business model concept lies within the traditional strategy lexicon of competitiveadvantage, we argue that the business model is a stand-alone concept in its own right because a business model is a model(Baden-Fuller and Morgan, 2010). Similarly, Markides and Sosa (2013) distinguish between market entry decisions andbusiness model choice. And we also explain how this view accords with the widespread recognition in the literature that abusiness model should be able to link two dimensions of firm activity d value creation and value capture (Amit and Zott,2001; Zott and Amit, 2010; Casadesus-Masanell and Ricart, 2010; Teece, 2010).

We have not mentioned technology in this conception of the business model. How do technology and business modelsinteract? Technology development can facilitate new business models d the most obvious historical example is the way theinvention and development of steam power facilitated the mass production business model. But, business model innovationcan also occur without technology development, as occurred in 1980s when the Japanese pioneered the “just in time”production system. In fact, business models and technologies regularly interact. For example, when Amazon was founded in1995, they applied new technology to make the traditional mail-order business model pioneered by Sears Roebuck work wellfor books. Amazon did not invent a new business model, nor did Easy-Jet (one of Europe’s most successful low-cost airlines)when it copied the business model pioneered by Southwest Airlines. Both Amazon and Easy Jet applied well-known businessmodel constructs and developed them in new contexts.

In contrast, Google’s two-sided dynamic search engine developed in 2003 was not just a technological leapd it was also abusiness model leap. Google used Adwords to provide an interface with advertisers (one side of the two sided platform)whose choices directly influenced the search experience of users on the other side of the platform. Google was probably thefirst company in the world to create the type of scalable dynamic two-sided platform modeled by Rochet and Tirole (2006).

0024-6301/$ – see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.lrp.2013.08.023

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426420

The novelty of their approach lay in linking the two sides in a constantly changing manner that allowed greater consumersatisfaction and greater revenues for any given set of users on each side of the platform.

Discussions of the effects of business models on performance do not always separate the effects of business modelinnovation from technological innovations (c.f. Zott and Amit, 2007; Casadesus-Masanell and Ricart, 2010). We know thattechnological innovation influences performance (cf. Bierly and Chakrabarti, 1996; Christensen and Bower, 1996; Zaheer andBell, 2005; and reviews by Evanschitzky et al., 2012; Hauser et al., 2006). But, to improve our understanding, we need a moreprecise appreciation of how innovation links to performance through the business model, and how changes in the businessmodel influence technological innovation. The fact that positive effects of technological innovation on business performanceare easily observed has diverted attention from questions about how business models change in the wake of innovation. Atthe same time, management theory requires more precision concerning the means by which business model changes enableand foster innovation.

This piece will first explore key relationships that are embedded in the business model construct, and then briefly reviewwhat we knowabout these relationships with a view to building a research agenda for the future.We begin by exploring whata novel business model is and hownew businessmodels are related to new technologies.We note that this approach of seeingthe business model as a model is similar to the logic of reasoning and understanding that exists in economics, biology andphysics. In each of these fields, as explained by philosophers of science, models are “manipulable instruments with which toreason and into which to enquire” and tools that “allow the user of the model to explore ideas” (Morrison and Morgan, 1999;Morgan, 2012). This allows us to assert that it is intellectually robust to ask: “Is business model innovation potentially separatefrom technological innovation?” d even though business model possibilities often rely on technology.

Building the framework

As noted, work on classifying business models has proceeded along two lines. First, there are researchers and com-mentators who see the business model concept as part of the strategy lexicon and intertwined with technology. They talk of“novel” and “efficient” business models if a new technology is incorporated into a business to produce a superior effect (e.g.,Zott and Amit, 2007; Osterwalder and Pigneur, 2010). Secondly, there are researchers, such as Teece (2010) or Baden-Fullerand Morgan (2010), who see the concept of the business model as potentially separable from technology and strategy andexamine how understanding business models and business model innovation might shed light on core strategy and tech-nology questions. This latter approach has the potential to answer the long-standing challenges posed by Chesbrough (2010),who asks when a novel technology requires a novel business model, and when the combination of a novel technology and anovel business model lead to competitive advantage.

Classification is necessary in order to understand innovation because only then can we appreciate what is meant bynew. Two major approaches to classification can be found in the literature. First, classification has been approachedtaxonomically by trying to build a picture from looking backwards at empirical work d taking the outputs or location ofthe use of the model as central. Thus, we have the classification of Wirtz et al., 2010, which stressed the difference betweencontent, commerce, context and connecting business models, and from Zott and Amit (2007), who emphasize the effi-ciency and novelty business models. But an alternative line of argument has emphasized the dimensions of the modelrather than its consequences d a classification that could be described as typological (Hempel, 1965). This type of clas-sification is more forward looking and has been at the heart of discussions that recognize two vital dimensions: Valuecreation and Value Capture. Value reaction identifies the customer or customers and how are they engaged (cf. McGrathand MacMillan, 2000), and value capture identifies how value is delivered and monetized (Teece, 2010). This classic two-part division of the business model (cf. Amit and Zott, 2001), leads to the possibility of a typological, theoretically-drivencategorization that can assist in identifying basic types. In line with this argument, we develop a typology with four di-mensions: customer identification, customer engagement, value delivery, and monetization; and we explore what thistypology does for our understanding of business model innovation and its relationship to technical innovation. Table 1gives examples of the classification of well-known firm-business model configurations. We explain the dimensionsmore fully below.

Customers

First, we address the customer identification dimensions of the business model. We stress that with modern technologicalpossibilities, it is essential that the business model identifies the users and the customers and indicate whether users pay forwhat they use or another group of customers actually pays (Teece, 2010). Historically, users almost always have paid, albeit inmany different ways usually at time of purchase or subsequently through a “razor-blade” model of usage charges. But withnewspapers, television, and finally the internet, technology has created the possibility that users may not pay for the servicesthey received payment instead being made by others such as advertisers, as indicated in Table 1. Two-sided platforms of thistype (Rochet and Tirole, 2006) are hybrid business models because they incorporate two value delivery systemsd one for theuser (such as a consumer that wants to search) and another for the customer who pays, such as a small firm that wants toplace an advertisement where it can be seen by a particular kind of consumer. The internet did not “invent” two-sidedplatforms d they have existed since before the 18th century d but it did facilitate their expansion (see Table 1 for theexample of a dynamic, advertising-supported search engine).

Table 1Examples of the Business Models

Fast food chain – franchisedBM

Boutique strategyconsultant BM

Defense contractorBM

Newspaper (1990s)BM

Search Engine BM

CUSTOMERIDENTIFICATION

Are users paying andif not who are theother customers?

SIMPLE BMUser pays withfranchisee as anintermediary

SIMPLE BMUser pays

SIMPLE BMUser is typicallythe government whopays

HYBRID BMReaders pay per copyAdvertisers contributebulk of revenues

HYBRID BMFree for users,but advertisers pay

CUSTOMERENGAGEMENT

“Taxi” or “Bus”

BUSScale based

TAXIBespoke projects

TAXIUsually project based

BUS Readers andadvertisersare given bus service

BUS for usersTAXI for advertisers

VALUE CHAIN LINKAGESIntegrated,hierarchyor networked

Highly tiered systemof suppliers andfranchisees,who are linkedhierarchically

Almost all value isdeliveredby the firm, littleoutsourcing

Complex system ofarrangements amongmany partners

Content and productionare typically hierarchicalbut sometimes network

Complex tightlycontrolledlinkagesorchestratedby firm

MONETIZATIONWhen, What and How

is money raised

COMPLEMENTARY ASSETSFranchisee collectsmoney fromconsumer andpasses on fee

VALUEOften priced on thebasis of fee plus shareof the value created

COSTStaged payments andoften cost pluscontract

TWO-SIDEDEveryone pays close topoint of use

TWO-SIDEDAdvertisers payafter service isdelivered

� Table reserved to Charles Baden-Fuller, 2013, reproduced by permission.

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 421

Customer engagement

Secondly, we address the question of customer engagement. This requires sensing what the customer-user or groups ofcustomer-users need, and establishing the value proposition for each of these groups perhaps using the process explained byMcGrath and MacMillan (2000), McGrath (2010), Day and Moorman (2010), and Day (2013). A long-standing distinctionbetween “project based offerings” and “pre-designed (scale) based offerings” d often described as the “taxi’ and “bus”systems d is useful in this context. Organizations such as consulting firms and movie makers use the taxi system to createvalue by interacting with specific clients to solve specific problems, while organizations such as automobile assemblers andproviders of fast food utilize the bus system and add value by producing “one-size-fits-all” goods or services in a repetitivemanner from a standardized, mass-production format.We suggest this distinction fits with the argument of Thompson (1967)and Drucker (1986), who proposed a distinction between firms organized in teams and mass production. The categories werefer to as “project” or “taxi” and “scale” or “bus” are more than just slogans; they are well established, economically-robust,model-based configurations that have been widely examined by scholars from communities both within and beyond man-agement. These models exhibit clear features; they typically have recognized processes andmechanisms and commonality inhow they utilize “knowledge” and “routines”.

The project-based approach is characterized bybespoke projects responding to customer needs. Its routines are designed tobe particularly effective at three things: dealing with non-routine complex tasks that require the repeated reconfiguration oforganizational structures; responding flexibly to changing client needs; and integrating diverse bodies of knowledge (Daviesand Brady, 2000; Hobday, 2000; Söderlund and Tell, 2010). Table 1 illustrates this with the example of a defense contractor.

In contrast, the “pre-designed” or “scale-based” approach is characterized by products made through scale-based systemsusing machines and routines that have a limited capacity to respond “flexibly” to unexpected client needs (e.g., Hounshell,1984; Chandler, 1990). The fast food hamburger chain discussed in Table 1 is an example of a scale-based business. Busi-ness systems using this approach also “integrate diverse bodies of knowledge”, but typically via different processes. There is aclear theoretical boundary between how project-based and scale-based organizations utilize knowledge in creating value(e.g., Chandler, 1990; Nightingale, 2000; Nightingale et al., 2011; Hobday, 2000).

Value delivery and linkages

The third component is the set of linkages between identifying the customer groups, and sensing their needs on the onehand, and monetization on the other. These linkages sometimes are described as value delivery, but they may go further thanthe traditional value chain, because a two-sided business model that has two sets of customers typically also involves twovalue chainsd one for each side of the market. These linkages can be described by the architecture of information flows andsystem governance (Amit and Zott, 2001; Casadesus-Masanell and Ricart, 2010). We do not discuss these dimensions in anydetail as they are generally very well understood. Important contributions to this idea come from the literature on verticalintegration (Williamson, 1985) and on hierarchy vs. network (Lorenzoni and Baden-Fuller, 1995), and research on other ar-rangements that can extend beyond the firm to upstream supplier networks, and downstream linkages with and amongcustomers.

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426422

Monetization

The last component of the business model is monetization, often labeled value capture. Discussions of monetization haveoften stopped with pricing ignoring important issues of timing and effectiveness which are paramount additional valuecapture dimensions for organizations. Concerning pricing, there are many other possibilities, including negotiated prices, andprice based on value delivered. One of the most important contributions to our understanding of pricing comes from Teece(1986), who stresses the role of complementary assets. Complementary assets can leverage monetizing opportunities,particularly in the case of the razor-blademodel. In cases such as the fast-food franchise-system noted in Table 1, the user paysthe complementary asset provider who in turn pays the provider.

In addition, there are important questions about when the money is collected d before the sale; at the point of sale; orafter the sale. A very important choice in the business model for durables is whether to rent a machine or sell it outright. Therental system implies a different form of customer engagement and different timing for collecting income that can be tailoredmore closely to value-based pricing.

Refining the innovation performance link

Strategy scholars have underplayed the role of business model choice in their search for establishing a link betweentechnology innovation and competitive advantage. The typical assumption that a radically improved product or service of-fering will over time automatically lead to increased profits for the innovating firm(s), ignores the enormous problems thatfirms face in working out the interdependencies between business model choice and technology effectiveness.

A given technology seldom operates in isolation from other technologies; interoperability is required in order to create theintended value. This is awell-recognized relationship, but it recently has becomemore intense, dynamic and uncertain, due tothe arrival of sophisticated information technology and greater availability of platform technologies. Those who assume asimple relationships between technology development and the performance outcomes for a firm or firms ignore themoderating influence of business model choice. Business model choice determines the nature of complementarity betweenbusiness models and technology and the paths to monetization. A poor choice can lead to low profits, a good choice to su-perior profits.

Many examples exist of these interdependencies. Business models for navigation systems, for example, present solutionsas standalone units or as mobile applications that take into account information shared by other drivers d such as Waze, amobile map application acquired by Google in 2013. In this example, the map and the satellite navigation systems of Wazelink users to each other via the Internet, and they can share local information. The technology’s main platform is the mobilephone operating system. The application also links to Facebook, which allows drivers to discover friends from their socialnetwork while on the road. Localized advertisement and newsmonetize the value created through whatWaze vice presidentElish describes as an “improved driving experience” (Elish, 2013). In the example of Waze, the business model choice de-termines the profitability of the technology. By choosing a two-sided business model that links customers with each otherand with advertisersd as opposed to a simple single sided business modeldWaze appears to have increased its value manytimes to nearly $1 billion.

Competitive dynamics not only influence product margins but also the viability of the business model. Recent work byEisenmann et al., 2011 analyzes competition between platform firms showing that survival of the entire platform depends oncomplementarity of the offering, which in turn depends on technology, features and interoperability. The interactions be-tween technology and business model are substantially more complex and more dynamic with two-sided business models(see for instance Casadesus-Masanell and Yoffie, 2007 onWintel; Casadesus-Masanell and Zhu, 2010 on two sided platforms).

We can explore this issue further with a simple example: the video game industry. Video games originally were played oneither a home computer or a specialist console. Technological innovations, in the form of better animation, better controls, orbetter visual experience, typically yielded profits for the innovator. Recently, the industry has become more complex.Although many games are still available via traditional routes, some of the most successful firms are offering games throughother channels, including the web. Firms often adopt a two-sided business model where most users pay nothing andmonetizing occurs through advertising and “freemium” pricing models. This development highlights the sensing dimensionof the business modeld firms are offering the same product or service to customers in different ways with different methodsof engagement and different monetization routes. These engagement and monetization structures have to adapt to thechanges taking place on the platform, including those initiated by providers of complementary assets in other sectors thatinfluence the other side of the platform.

Zynga, a leading web-based game publisher in 2013, offers the popular game FarmVille in the freemium mode. Users canplay for free and acquire in-game assets, if they wish. Game play happens via a social network, such as Facebook. Zynga earnsmoney from a small fraction of its users, depending largely on social networks to do business, earning money from cross-sellers and advertisers. Zynga chose Facebook for its main platform, and took advantage of a number of critical featuresthe platform offers, such as comparability of game scores among friends and advertisements. But, Zynga also has to considerwhether and when it makes sense to switch between platforms and which new features of Facebook are appropriate for thecontext of gaming. Product extensions that take advantage of user content shared on the social network touch upon theelement of customer engagement for the service provider, and critically depend on available technology that may or may notbe appropriated. The negotiation processes between the involved parties about innovation in gaming and access to platform

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 423

technology also impactmonetization through decisions about questions such as whether financial transactions should run viaFacebook, or bypass the platform.

Therefore, an important research agenda for technology strategy scholars is to unpick the interdependencies betweenbusiness model choice, technology development, and success. Theory building needs to be matched by skillful empiricalwork. Making business model choice a moderator, and including the factors that influence business model change in a dy-namic manner, will lead to a better understanding of the fundamentals of the relationship. And it will also allow strategists tocomment more succinctly and usefully on key contingencies – such as, why so many innovative products fail; how successfulfirms conceive the relationship between technology and business model; and how they conceive the dynamics of the processof business model adjustment.

Developing the right technology

A number of actors d including systems integrators (Prencipe et al., 2003), entrepreneurs (Garud and Karnøe, 2003), orusers (Von Hippel, 1988)d play a key role in trying to answer the long-standing question: What determines the direction oftechnology evolution? These actors will be driven by the cognitive frames they hold that connect perceived customer desiresto the innovation agenda. Business models are not just statements of economic linkages but also cognitive devices; businessmodels held in the minds of these actors influence technological outcomes.

These cognitive business models exist even before the technology is designed and the products are built. At one extreme,the developer’s business model could be something very simple and formed by the developer’s own preferences concerningwho the customer is and the method of customer engagement (Denyer et al., 2011; Haefliger et al., 2011). Or, it may be drivenby the current belief system of the company (e.g., the discussion of Kodak by Tripsas and Gavetti, 2000; or Xerox byChesbrough, 2010). At the other extreme, the actor may have a very rich and free-flowing view of the world, influenced bydeep knowledge and understanding of social and technical possibilities and unencumbered by immediate external biases. It isnot the purpose of this paper to explore how the cognitive frames come about; this is a separate concern (e.g., Baden-Fullerand Mangematin, 2013 forthcoming). Our purpose is to explain why differences in these business model frames producewidely different outcomes.

We highlight two important factors in the business model that influence development: the role of openness, and the roleof users. Openness refers to the permeability of the company boundaries. Chesbrough (2003) initially coined the term “openinnovation” to acknowledge the potential value for firms in buying and selling intellectual property that has not yet reachedthe product stage (Arora et al., 2001). Openness now has come to have a broader meaning with recognition that processtechnology may not be patented and is difficult to protect. It may nonetheless be valuable for users or even competitors toshare technology without asking for compensation (Henkel, 2006), because sharing may lead to learning and to the estab-lishment of communities with similar, professional interests.

We argue that it is not just openness that matters in determining technological trajectories, but the connectivity betweenopenness and user engagement d again a business model choice. The paternalistic view that management knows what isbest for the customers is being challenged by the growth and success of mass customization business models (where masscustomization is taken tomean organizational responsiveness to customer requests for differentiation – see Ogawa and Piller,2006). This responsiveness can be fostered by crowd sourcing and by open and user innovation (Jeppesen and Lakhani, 2010;Hienerth et al., 2011), as well as by the advent of information technology that makes these new business models scalable.

This responsiveness can allow involvement by customers deep into the fabrication process, and offer toolkits to customersthat allow them access and express choice in technology development and design (Franke and Piller, 2004). These in-volvements have been associated with new discoveries and innovations across many industries, from extreme sports(Baldwin et al., 2006) to software (Bonaccorsi et al., 2006).

For example, we know that software development flourishes in online communities (Roberts et al., 2006) and thatcompanies have increasingly taken advantage of linking and liaising with these communities to share technology andknowledge (Colombo et al., 2013; Dahlander and Magnusson, 2008). By definition, choice of engagement with onlinecommunities and the integration of customers into the development process is a business model choice. Customerengagementmay occur through a tool kit of standard choices, or through contributed innovative ideas and technical solutions(Franke and Piller, 2004; Füller et al., 2008). Greater customer engagement leads to increased value creation for both sides(Franke et al., 2010). Firms contributing to online communities as clients and developers of software products share theiradaptations of the product with vendors and competitors, for improved next release and to increase system good will(Henkel, 2009). Thus, we can see that there is an interaction between business model choice and the direction of technologydevelopment.

And in another example, that of the T-shirt, we see how business model choice about openness and scalability hasinfluenced development. The textile industry was at the core of the industrial revolution in the 19th century. Throughoutmost of the subsequent history of the industry, producers followed a traditional business model of customer sensing andengagement that involved professionals designing, large scale manufacturers assembling and the results sold in shops and bymail order. In the late 20th century, more advanced printing and stitching machines brought about the option of custom-ization at the final stage of production. Customers could bring their own designs and have T-shirts made to order. Firms thatadopted this novel business model became highly successful and changed the industry. More recently, the T-shirt maker

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426424

Threadless has followed an even newer business modeld inviting both designers and consumers to directly interact, makingT-shirts for themselves offering the same designs to others for cash prize rewards.

Firms such as Threadless exploit novel two sided business models that match designers to consumers. In the newThreadless system, freelance professional designers work with far greater freedom thanwas possible in the old system, and alarge group of consumers experience what it is like to be a designer giving them new experiences and pleasure. This hasopened up a new innovation pathd the possibility of novel designs that become popular faster than traditional designer-ledor user-led methods. Threadless can also minimize production risks by not producing shirts unless enough customers havepre-ordered the designs they endorse.

Thus far we have talked of the way that business model choice influences technological and firm development, but it ispertinent to ask if technology also acts on business model possibilities. Looking at the T-shirt example, wewould argue that itmight have been possible to set up a similar business model before the advent of the Internet. Designs could have been postedin a public space and pre-orders could be made known to the manufacturer. In fact, this business model did exist. Fashiondesign has always involved elite circles of customers who influence design. However, the scale at which this model workstoday is unprecedented. Scale influences both the reach of opennessd so that anyone with access to the Internet can submitdesigns and vote and buy d and, it influences the possibilities for collaboration. Published designs receive comments fromregistered customers and designers can react, adapt and resubmit their designs.

Management agenda

Managers need to be creative in the face of this complex interplay between innovation and business model elements. Theycan approach these problems experimentally (McGrath, 2010) or by following recipes (e.g. Sabatier et al., 2010). Models alsomay help them expand their reasoning (Baden-Fuller and Morgan, 2010), and recognize the value of involving others such asthe developer of technology in the design of the business model. This is what Baldwin and Clark (2006) call the “architectureof participation”.

Managers need to decide who should be involved, where control should be exercised in these domains and where self-organization should lead the way (von Krogh et al., 2012). Different stakeholders perceive different domains as more cen-tral or dominant. Technology developers understand the agenda and possibilities for a technology to be used butmaymiss theimplications for monetization or market demand. On the other hand, marketing experts may hold deep insights intocustomer behavior but may not understand what a given technology could be expected to deliver.

Taking an ecosystem perspective may also help because it focuses attention on how the systems integrators need to formexpectations about the scientific and technological fields underlying the components and sub-systems (Dosi et al., 2003).Even where innovation is not “open” beyond the specification of interfaces, the supplier needs to understand and link to thetechnological and organizational environment within which their components are sold in order to understand the market.Systems integrators, platforms, and multi-sided markets share what is sometimes referred to as a business ecosystem. Formanagers, the ecosystems perspective holds the promise of opening up the wider entrepreneurial and collaborative spacethat a new technology affords d and provides room for novel business models to succeed.

Discussion

In summary, we first noted that choice of business model influences the way in which technology is monetized and theprofitability for the relevant firms. We then noted that the business model frames managers, entrepreneurs, and developershold in their heads also determine the way in which technology gets developed d and that these connections are capable ofbeing very powerful. This means that the connection between business model choice and technology is two-way andcomplex d something that has received little attention. But this relationship is capable of being unpicked and understood(see Jacobides and Billinger, 2006, on make-or-buy decisions). And we also recognize that technology will itself influencebusiness model possibilities.

This means that technology from other sectors such as information technology influences the way in which a businessmodel can be created and adapted. The mobile phone application is one such technology that serves as a process innovationfor gaming or navigation. For example, Waze and Zynga (described above), use app technology extensively. Waze reliesentirely on mobile phone applications, and Zynga needs application technology to work reliably on tablet computers such asthe Apple iPad series of hardware. If performance improvements rely on both process innovation and business model changesthe traditional S-curve in technology management needs to be re-visited.

Whether we are trying to understand the past or influence the future, we need to model the link between technologydevelopment and firm performance, taking into account competitive dynamics, the influence of technology on businessmodel innovation, and the organization of technology development. In many markets d especially those influenced by newdigital technologyd it is not obvious how to develop and appropriate technology because customer demands are shifting andtechnology possesses agency of its own (Orlikowski, 1992; Leonardi, 2011). The business model may have to change in orderto appropriate features of a technology that create customer value. Also, elements of the model may change in order to allowtechnology to be developed that fits customer needs or that emerges from the customer directly (Hienerth et al., 2011).

We note that a larger theoretical issue behind this observation is to what extent the organization of technology can andshould mirror the structure of the underlying technology. The question is known as the “mirroring hypothesis” (Colfer and

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426 425

Baldwin, 2010). Sosa et al. (2004, 2007) demonstrated for large engineering projects that an alignment of team structureswith technical modules enhances development effectiveness and saves costs. This points to an important open question, towhat extent business model elements can and should map the modularity of the technology applied and under development,and vice versa. Modularity has a long history in strategy and innovation (Alexander, 1964; Garud and Kumaraswamy, 1995;Baldwin and Clark, 2000), and, both cognitively and practically, it offers insights that link the stage of technology develop-ment with the organization of innovation and customer engagement (Brusoni and Prencipe, 2006; Baldwin & Clark, 2006,Argyres, 1999). Modularity is a model of technology development that could help explain technological development and thejoint implications of changing customer demands and technological evolution for the business model.

Lastly, business models are recipes and represent tools for management. They can be blindly replicated or applied tosettings that deserve attention to difference and creativity. Business models contain theory and assumptions about customerbehavior and agency that may not hold in a specific situation. Some of the key assumptions deal with rationality in decisionmaking. Recent work on performativity shows the existence of purposeful efforts to uphold rationality in organizations(Cabantous and Gond, 2011). Rationality may be served by modeling but may not necessarily help business and managerialdecision-making in practice. Creativity and innovation emerge from passion and non-rational pursuits (Rindova et al., 2009),or from unusual sensitivity to discover and disclose (Spinosa et al., 1997) in business, as much as in technology or the arts.Thus, we call for drawing this distinction more clearly in business model research to recognize where rational decision-making deserves its rightful place, and where other, possibly finer forms of deliberation and perception should guidemanagerial action.

The authors can be reached at: c.baden-fuller@city.ac.uk, and haefliger@city.ac.uk. We would like to acknowledge helpfulcomments from Paul Nightingale, Andre Spicer, Mary Morgan, Vincent Mangematin and the financial support of the UKResearch Council (EP/K039695/1 Building Better Business Models). We thank Jim Robins for encouragement and theopportunity.

References

Alexander, C., 1964. Notes on the Synthesis of Form. Harvard University Press, Cambridge, MA.Amit, R., Zott, C., 2001. Value creation in e-Business. Strategic Management Journal 22, 493–520.Argyres, N.S., 1999. The impact of information technology on coordination: evidence from the B-2. Organization Science 10 (2), 162–180.Arora, A., Fosfuri, A., Gambardella, A., 2001. Markets for technology and their implications for corporate strategy. Industrial and Corporate Change 10 (2),

419–451.Baden-Fuller, C., Morgan, M., 2010. Business models. Long Range Planning 43, 156–171.Baden-Fuller, C., Mangematin, V., 2013-08-16. Business Models: a Challenging Agenda. Cass Business School. Strategic Organization, SO!apbox

(forthcoming).Baldwin, C.Y., Clark, K.B., 2000. Design Rules. In: The Power of Modularity, vol. 1. MIT Press, Cambridge, MA.Baldwin, C., Hienerth, C., Von Hippel, E., 2006. How user innovations become commercial products: a theoretical investigation and case study. Research

Policy 35 (9), 1291–1313.Baldwin, C., Clark, K., 2006. The architecture of participation: does code architecture mitigate free riding in the open source development model? Man-

agement Science 52 (7), 1116–1127.Bierly, P., Chakrabarti, A., 1996. Generic knowledge strategies in the US pharmaceutical industry. Strategic Management Journal 17, 123–135.Bonaccorsi, A., Giannangeli, S., Rossi, C., 2006. Entry strategies under competing standards: hybrid business models in the open source software industry.

Management Science 52 (7), 1085–1098.Brusoni, S., Prencipe, A., 2006. Making design rules: A multidomain perspective. Organization Science 17 (2), 179–189.Cabantous, L., Gond, J.-P., 2011. Rational decision making as performative praxis: explaining Rationality’s Éternel Retour. Organization Science 22 (3),

573–586.Casadesus-Masanell, R., Ricart, J., 2010. From strategy to business models and on to tactics. Long Range Planning 43, 195–215.Casadesus-Masanell, R., Yoffie, D.B., 2007. Wintel: cooperation and conflict. Management Science 53 (4), 584–598.Casadesus-Masanell, R., Zhu, F., 2010. Strategies to fight Ad-sponsored rivals. Management Science 56 (9), 1484–1499.Chandler, A.D., 1990. Scale and Scope. Harvard University Press, Cambridge.Chesbrough, H.W., 2003. Open Innovation: the New Imperative for Creating and Profiting from Technology. Harvard Business Press.Chesbrough, H.W., 2010. Business model innovation: opportunities and barriers. Long Range Planning 43 (2–3), 354–363.Christensen, C.M., Bower, J.L., 1996. Customer power, strategic investment, and the failure of leading firms. Strategic Management Journal 17 (3), 197–218.Colfer, L., Baldwin, C.Y., 2010. The Mirroring Hypothesis: Theory, Evidence and Exceptions. Available at. Social Science Research Network, Rochester, NY

http://papers.ssrn.com/abstract¼1539592 (accessed 15.08.13).Colombo, M.G., Piva, E., Rossi-Lamastra, C., 2013. Authorising employees to collaborate with communities during working hours: when is it valuable for

firms? Long Range Planning 46 (3), 236–257.Dahlander, L., Magnusson, M., 2008. How do firms make use of open source communities? Long Range Planning 41 (6), 629–649.Davies, A., Brady, T., 2000. Organizational capabilities and learning in complex product systems: towards repeatable solutions. Research Policy 29, 931–953.Day, G., Moorman, C., 2010. Strategy from the Outside-in: Profiting from Customer Value. McGraw Hill, New York.Day, G., 2013. Innovation Prowess. Wharton Digital Press, Philadelphia.Demil, B., Lecocq, X., 2010. Business model evolution: in search of dynamic consistency. Long Range Planning 43, 227–246.Denyer, D., Parry, E., Flowers, P., 2011. “Social”, “open” and “participative”? Exploring personal experiences and organisational effects of enterprise 2.0 use.

Long Range Planning 44 (5–6), 375–396.Dosi, G., Hobday, M., Marengo, L., Prencipe, A., 2003. The economics of systems integration: towards an evolutionary interpretation. In: Prencipe, A.,

Davies, A., Hobday, M. (Eds.), The Business of Systems Integration. Oxford University Press, pp. 95–113.Drucker, P., 1986. What is a Business? Chapter 6 in Management, Tasks, Responsibilities, Practices. Truman Talley Books, New York.Eisenmann, T., Parker, G., Van Alstyne, M., 2011. Platform envelopment. Strategic Management Journal 32 (12), 1270–1285.Elish, Y., 2013. CNET, May 30, 2013. URL http://news.cnet.com/8301-1023_3-57586751-93/waze-directs-drivers-to-facebook-events/.Evanschitzky, H., Eisend, M., Calantone, R.J., Jiang, Y., 2012. Success factors of product innovation: an updated meta-analysis. Journal of Product Innovation

Management 29, 21–37.Franke, N., Piller, F., 2004. Value creation by toolkits for user innovation and design: the case of the watch market. Journal of Product Innovation Man-

agement 21 (6), 401–415.Franke, N., Schreier, M., Kaiser, U., 2010. The “I designed it myself” effect in mass customization. Management Science 56 (1), 125–140.

C. Baden-Fuller, S. Haefliger / Long Range Planning 46 (2013) 419–426426

Füller, J., Matzler, K., Hoppe, M., 2008. Brand community members as a source of innovation. Journal of Product Innovation Management 25 (6), 608–619.Garud, R., Kumaraswamy, A., 1995. Technological and organizational designs for realizing economies of substitution. Strategic Management Journal 16,

93–109.Garud, R., Karnøe, P., 2003. Bricolage versus breakthrough: distributed and embedded agency in technology entrepreneurship. Research Policy 32 (2),

277–300.Haefliger, S., Monteiro, E., Foray, D., Von Krogh, G., 2011. Social Software and Strategy. Long Range Planning 44 (5–6), 297–316.Hauser, J., Tellis, G.J., Griffin, A., 2006. Research on innovation: a review and agenda for marketing science. Marketing Science 25 (6), 687–717.Hempel, C.G., 1965. Fundamentals of taxonomy, and typological methods in the natural and social sciences. In: Aspects of Scientific Explanation. Macmillan,

New York, pp. 137–172.Henkel, J., 2006. Selective revealing in open innovation processes: the case of embedded Linux. Research Policy 35 (7), 953–969.Henkel, J., 2009. Champions of revealingdthe role of open source developers in commercial firms. Industrial and Corporate Change 18 (3), 435–471.Hienerth, C., Keinz, P., Lettl, C., 2011. Exploring the nature and implementation process of user-centric business models. Long Range Planning 44 (5–6),

344–374.Hobday, M., 2000. The project-based organisation: an ideal form for managing complex products and systems? Research Policy 29 (7–8), 871–893.Hounshell, D.A., 1984. From the American System to Mass Production: 1800–1932. Johns Hopkins University Press, Baltimore.Jacobides, M.G., Billinger, S., 2006. Designing the boundaries of the firm: from “make, buy, or ally” to the dynamic benefits of vertical architecture. Or-

ganization Science 17 (2), 249–261.Jeppesen, L.B., Lakhani, K.R., 2010. Marginality and problem-solving effectiveness in broadcast search. Organization Science 21, 1016–1033.von Krogh, G., Nonaka, I., Rechsteiner, L., 2012. Leadership in organizational knowledge creation: a review and framework. Journal of Management Studies

49 (1), 240–277.Leonardi, P.M., 2011. When flexible routines Meet flexible technologies: affordance, constraint, and the imbrication of human and material agencies. MIS

Quarterly 35 (1), 147–167.Lorenzoni, G., Baden-Fuller, C., 1995. Creating a strategic centre to manage a web of partners. California Management Review 37 (3), 146–163.Markides, C., Sosa, L., 2013. Pioneering and first mover advantages: the importance of business models. Long Range Planning 46 (4-5), 325–334.McGrath, R., MacMillan, I., 2000. Discovery Driven Planning. Harvard Business Press, Boston.McGrath, R., 2010. Business models: a discovery driven approach. Long Range Planning 43, 247–261.Morgan, M.S., 2012. Modelling as a Method of Enquiry – Chapter 1 of, M.S. Morgan, The World in the Model. Cambridge University Press, Cambridge and

New York.Morrison, M., Morgan, M.S., 1999. Mediating Instruments. Chapter 2. In: Morgan, M.S., Morrison, M. (Eds.), Models as Mediators. Cambridge University

Press, pp. 10–37. Ideas in Context Series.Nightingale, P., Baden-Fuller, C., Hopkins, M.M., 2011. Projects, project capabilities and project organizations. In: Cattani, G., Ferriani, S., Frederiksen, L.,

Täube, F. (Eds.), Project-Based Organizing and Strategic Management (Advances in Strategic Management, vol. 28). Emerald Group Publishing Limited,pp. 215–234.

Nightingale, P., 2000. The product-process-organisation relationship in complex development projects. Research Policy 29 (7–8), 913–930.Ogawa, S., Piller, F.T., 2006. Reducing the risks of new product development. MIT Sloan Management Review 47 (2).Orlikowski, W.J., 1992. The duality of technology – rethinking the concept of technology in organizations. Organization Science 3 (3), 398–427.Osterwalder, A., Pigneur, Y., 2010. Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. John Wiley & Sons.Prencipe, A., Davies, A., Hobday, M., 2003. The Business of Systems Integration. Oxford University Press.Rindova, V., Barry, D., Ketchen, D.J., 2009. Entrepreneuring as Emancipation. Academy of Management Review 34 (3), 477–491.Roberts, J., Hann, I., Slaughter, S., 2006. Understanding the motivations, participation, and performance of open source software developers: a longitudinal

study of the Apache projects. Management Science 52 (7), 984–999.Rochet, J.-C., Tirole, J., 2006. Two-sided markets: a progress report. Rand Journal of Economics 37 (3), 645–667.Sabatier, V., Rouselle, T., Mangematin, V., 2010. From recipe to dinner: business model portfolio in the European biopharmaceutical industry. Long Range

Planning 43, 431–447.Söderlund, J., Tell, F., 2010. The P-form Organization: Contingencies, Characteristics, and Challenges. unpublished mimeo.Sosa, M., Eppinger, S., Rowles, C., 2004. The misalignment of product architecture and organizational structure in complex product development. Man-

agement Science 50 (12), 1674–1689.Sosa, M., Eppinger, S., Rowles, C., 2007. Are your engineers talking to one another when they should? Harvard Business Review 85 (11), 133–142.Spinosa, C., Flores, F., Dreyfus, H.L., 1997. Disclosing New Worlds: Entrepreneurship, Democratic Action, and the Cultivation of Solidarity. MIT Press.Teece, D.J., 1986. Profiting from technological innovation: implications for integration, collaboration, licensing and public policy. Research Policy 15 (6),

285–305.Teece, D.J., 2010. Business models, business strategy, and innovation. Long Range Planning 43, 172–194.Thompson, J.D., 1967. Organizations in Action. McGraw Hill, New York.Tripsas, M., Gavetti, G., 2000. Capabilities, cognition, and inertia: evidence from digital imaging. Strategic Management Journal 21 (10–11), 1147–1161.Von Hippel, E., 1988. The Sources of Innovation. Oxford University Press, New York.Williamson, O.E., 1985. The Economic Institutions of Capitalism: Firms, Markets, Relational Contracting. Free Press, New York.Wirtz, B.W., Schilke, O., Ullrich, S., 2010. Strategic development of business models: implications of the web 2.0 for creating value on the Internet. Long

Range Planning 43 (2–3), 272–290.Zaheer, A., Bell, G.G., 2005. Benefiting from network position: firm capabilities, structural holes, and performance. Strategic Management Journal 26 (9),

809–825.Zott, C., Amit, R., 2007. Business model design and the performance of entrepreneurial firms. Organization Science 18, 181–199.Zott, C., Amit, R., 2010. Business model design: an activity system perspective. Long Range Planning 43, 216–226.Zott, C., Amit, R., Massa, M., 2011. The business model: recent developments and future research. Journal of Management 37, 1019–1042.

Biographies of authors

Charles Baden-Fuller is Centenary Professor of Strategy, Cass Business School, City U. London, and Senior Fellow,Wharton School, U. Penn. He is also a Fellowof the Strategic Management Society, and winner of the Lord Mayor of London’s Chancellor’s Prize at City University. He is very widely published andrecognised as a thought leader on the topics of rejuvenating mature businesses, cognition and competition, high technology entrepreneurship and businessmodels. He advises senior executives and boards on topics of strategy in private international businesses and not-for profit organisations. He was editor-in-chief of Long Range Planning from 1999 to 2010. Email: C.Baden-fuller@city.ac.uk

Stefan Haefliger is Reader in Innovation at Cass Business School. He has published in Management Science, Research Policy, and MIS Quarterly, on knowledgereuse and private-collective innovation contributing to a deeper understanding of the development strategies and practices of open source softwaredevelopers as well as the entrepreneurial consequences of user innovation. He is an associate editor for Long Range Planning. Email: haefliger@city.ac.uk